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Automated Explosive Detection Systems utilizing Computed Tomography perform a series X-ray scans of passenger bags being checked in at the airport, and produce various 2-D projection images and 3-D volumetric images of the bag. The determination as to whether the passenger bag contains an explosive and needs to be searched manually is performed through trained Transportation Security Administration screeners following an approved protocol. In order to keep the screeners vigilant with regards to screening quality, the Transportation Security Administration has mandated the use of Threat Image Projection on 2-D projection X-ray screening equipment used at all US airports. These algorithms insert visual artificial threats into images of the normal passenger bags in order to test the screeners with regards to their screening efficiency and their screening quality at determining threats. This technology for 2-D X-ray system is proven and is widespread amongst multiple manufacturers of X-ray projection systems. Until now, Threat Image Projection has been unsuccessful at being introduced into 3-D Automated Explosive Detection Systems for numerous reasons. The failure of these prior attempts are mainly due to imaging queues that the screeners
pickup on, and therefore make it easy for the screeners to discern the presence of the threat image and thus defeating the intended purpose. This paper presents a novel approach for 3-D Threat Image Projection for 3-D Automated Explosive Detection Systems.
The method presented here is a projection based approach where both the threat object and the bag remain in projection sinogram space. Novel approaches have been developed for projection based object segmentation, projection based streak reduction used for threat object isolation along with scan orientation independence and projection based streak generation for an overall realistic 3-D image.
The algorithms are prototyped in MatLab and C++ and demonstrate non discernible 3-D threat image insertion into various luggage, and non discernable streak patterns for 3-D images when compared to actual scanned images.
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